Person Re-Identification (ReID) has long struggled with the semantic gap between low-level visual features and high-level identity concepts. While Vision-Language Models (VLMs) offer promising semantic understanding, existing methods typically adopt a static "one-pass" paradigm, converting images to text once for retrieval. This approach suffers from two critical flaws: Information Bottleneck, where converting rich visuals into text causes detail loss, and Open-Loop Failure, where initial hallucinations propagate without recourse. To address this, we propose Auto-ReID, a novel framework that reformulates ReID as an iterative "Think-and-Refine" process. We first introduce a Hierarchical Progressive Tuning strategy to transform a generic VLM into a specialized Re-ID expert. During inference, we deploy a closed-loop architecture comprising a Reasoner for structured attribute extraction, a Hybrid Retriever that anchors dynamic semantic queries with stable visual features to prevent drift, and a Corrector that deconstructs and verifies candidates to iteratively optimize the search. Extensive experiments on ReID datasets demonstrate that our method significantly outperforms state-of-the-art approaches, particularly in complex occlusion scenarios.

Text-based person anomaly search retrieves specific behavioral events from surveillance archives using natural-language queries. Although recent pose-aware methods align geometric structures well, they face a fundamental Pose-Semantic Gap: semantically different actions can share similar skeletal geometries. While Multimodal Large Language Models (MLLMs) can reduce this ambiguity, using them for large-scale retrieval is computationally prohibitive. We propose the Structure-Semantic Decoupled Cascade (SSDC) framework, which decouples retrieval into two stages: (1) Structure-Aware Coarse Retrieval, where a lightweight model quickly filters candidates by skeletal similarity; and (2) Detective Squad Interaction, a multi-agent semantic verification module. The squad consists of a Detective for fast binary filtering, an Analyst for evidence extraction, and a Writer for semantic synthesis. Finally, we re-rank candidates by fusing the synthesized captions with structural priors. Experiments on the PAB benchmark show that SSDC achieves state-of-the-art performance by balancing efficiency and semantic reasoning.

Multimodal Sentiment Analysis (MSA) models typically suffer significant performance degradation under domain shifts. While Test-Time Adaptation (TTA) aims to mitigate this, existing discriminative approaches often succumb to “confident but wrong” predictions on out-of-distribution samples. Conversely, generative models offer robust calibration but incur prohibitive computational costs. To bridge this gap, we propose GD-Adapt (Generative-Discriminative Adaptation), a novel TTA framework that harmonizes the robustness of generative diffusion models with the efficiency of discriminative regression networks via Bayesian Diffusion Distillation (BDD). Specifically, we introduce Auxiliary Generative Regularization (AGR) during pretraining to enforce manifold-aware feature learning. Extensive experiments across five cross-domain scenarios demonstrate our method’s superiority. For instance, on the challenging MOSI to SIMS shift, GD-Adapt reduces Mean Absolute Error (MAE) from 0.6872 to 0.5673 and boosts binary accuracy by 5.81 percentage points (reaching 57.33%). Notably, in scenarios such as SIMS to MOSI, we achieve an 11.18-point gain over the non-adapted baseline.

Text-based person search (TBPS) enables the retrieval of person images from large-scale databases using natural language descriptions, offering critical value in surveillance applications. However, a major challenge lies in the labor-intensive process of obtaining high-quality textual annotations, which limits scalability and practical deployment. To address this, we introduce two complementary modules: Multi-Turn Text Generation (MTG) and Multi-Turn Text Interaction (MTI). MTG generates rich pseudo-labels through simulated dialogues with MLLMs, producing fine-grained and diverse visual descriptions without manual supervision. MTI refines user queries at inference time through dynamic, dialogue-based reasoning, enabling the system to interpret and resolve vague, incomplete, or ambiguous descriptions—characteristics often seen in real-world search scenarios. Together, MTG and MTI form a unified and annotation-free framework that significantly improves retrieval accuracy, robustness, and usability. Extensive evaluations demonstrate that our method achieves competitive or superior results while eliminating the need for manual captions, paving the way for scalable and practical deployment of TBPS systems.

Zero-shot singing voice synthesis (SVS) with style transfer and style control aims to generate high-quality singing voices with unseen timbres and styles (including singing method, emotion, rhythm, technique, and pronunciation) from audio and text prompts. However, the multifaceted nature of singing styles poses a significant challenge for effective modeling, transfer, and control. Furthermore, current SVS models often fail to generate singing voices rich in stylistic nuances for unseen singers. To address these challenges, we introduce TCSinger, the first zero-shot SVS model for style transfer across cross-lingual speech and singing styles, along with multi-level style control. Specifically, TCSinger proposes three primary modules: 1) the clustering style encoder employs a clustering vector quantization model to stably condense style information into a compact latent space; 2) the Style and Duration Language Model (S&D-LM) concurrently predicts style information and phoneme duration, which benefits both; 3) the style adaptive decoder uses a novel mel-style adaptive normalization method to generate singing voices with enhanced details. Experimental results show that TCSinger outperforms all baseline models in synthesis quality, singer similarity, and style controllability across various tasks, including zero-shot style transfer, multi-level style control, cross-lingual style transfer, and speech-to-singing style transfer.